Computer-aided diagnosis for World Health Organization-defined chest radiograph primary-endpoint pneumonia in children

Abstract

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Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review

“…The most important reason is that the chest radiographs of COVID-19 patients must be reviewed by highly trained specialists, which creates large amounts of work for those specialists. Further, it is very difficult to read these images because pneumonia is normally revealed over one or more areas of increased opacity [ 39 ]. This increase may be due to a reduction of the ratio of gas to soft tissue (lung parenchyma, stroma, and blood) in the lungs.…”

“…Texture abnormalities are characterized by diffuse changes [ 45 ]. In these situations, texture analysis may be useful for assigning a probability to each location in the lung fields [ 39 ].…”

Study protocol and design for the assessment of paediatric pneumonia from X-ray images using deep learning

“…During the training process, the feature extractors near the model input learn primitive image features such as edges, whereas feature extractors deeper into the model learn domain-specific relationships important for the diagnostic task. CNNs have successfully been applied to assist in the detection and classification of human diseases like breast cancer, 7 8 tuberculosis, 9 diabetic retinopathy 10 and pneumonia [11][12][13] directly from images. In some instances, these systems performed as well as trained health professionals.…”

“…A small paediatric dataset of 858 chest X-ray images which was interpreted for PEP by three readers 2 was used to train there model, which achieved a 0.850 auROC. 13 Our study will use 10 000 images interpreted for PEP. Furthermore, by specifying the features that are to be used by a computational prediction method, one may potentially omit features that can be directly learnt from chest X-ray images, which will likely negatively impact model performance.…”

Artificial intelligence as an emerging diagnostic approach in paediatric pulmonology

“…While AI for diagnosing respiratory diseases is quickly developing in adults, up to now, its application in children has been limited to the detection of consolidation on chest X-ray images, 6 therefore aiding the diagnosis of pneumonia. 7 However, the opportunities to apply AI technologies in the field of childhood respiratory diseases are quite broad. Apart from supporting the clinician in the detection of radiological findings that are likely to be missed, AI might be useful to screen which tests have to be rapidly evaluated to ensure that children with potentially dangerous imaging findings are seen first, allowing for workflow optimization.…”